Fact-checking has emerged globally to combat misinformation and ensure accuracy of information. In Pakistan, it is very intricate due to politically polarized media landscape. This study explores the fact-checking practices of Pakistani journalists amid rampant spread of fake news. Using Qualitative research approach, the researchers conducted semi-structured interviews of 21 journalists (male/female) from mainstream media (electronic & print) and digital media news outlets selected through purposive sampling. The journalists were investigated about methodologies being employed and challenges being faced in fact-checking process. They were also inquired about the impact of fact-checking on quality of journalism and the influence of source's perspective and context of story during fact-checking. Findings reveal that, despite recognizing fact-checking as a professional and ethical commitment, its effective implementation is hindered by organizational policies, external pressures, the absence of proper mechanisms, time constraints, limited professional development training, and the inaccessibility of authentic data and reliable sources. The research suggests that fact-checking is not just a technical or ethical tool in journalism but a democratic requirement in Pakistan. Hence, adopting this practice profusely will reinforce watchdog role of media, and also help to mitigate polarization in social and political spheres by making people more informed citizens.
Abstract Why do foreign investors exit Central and Eastern Europe (CEE) despite formal alignment with EU institutional standards? We argue that divestment reflects not cyclical volatility but strategic responses to institutional fault lines, the non-linear interaction between de jure legal frameworks and de facto enforcement credibility. Analysing panel data (1990–2024) for 20 CEE economies through a hybrid econometric–machine learning framework (ARDL bounds testing, Dumitrescu–Hurlin causality, Random Forest with SHAP diagnostics), we find that: (1) rule-based governance (corruption control) Granger-predicts divestment (Z = 3.897, p = 0.001), whereas formal rule-of-law indicators show ambiguous effects; (2) macroeconomic instability robustly elevates exit risk (β = 0.277, p = 0.001); and (3) trade openness and human capital amplify divestment only when enforcement credibility is weak. Economic scale dominates predictive power (72.0%) but reflects historical FDI exposure, not causal drivers. Critically, machine learning reveals that divestment tipping points emerge from combinations of high inflation, low corruption control, and high openness, patterns invisible to linear models. These findings reframe capital flight as a rational response to institutional dissonance rather than market failure. For policymakers, the implication is clear: EU cohesion policy should shift from formal harmonisation towards performance-based governance that prioritises verifiable anti-corruption enforcement and macroeconomic credibility.
Reliable detection of railway track faults is essential for preventive maintenance and safety. We introduce RailNet, a deep learning-based convolutional architecture that uses a DenseNet121 backbone as its feature extractor together with a streamlined classification head tailored to rail imagery. RailNet is fine-tuned with a task-aware augmentation policy designed to mimic in-field conditions (viewpoint change, illumination variation, and occlusions) and includes built-in gradient-weighted class activation mapping (Grad-CAM) interpretability to highlight defect regions that drive predictions. An ablation study quantifies the contribution of key head components (batch normalisation, dropout, and layer depth) to generalisation. RailNet is evaluated on a labelled dataset of faulty and non-faulty track images; it achieves 96% accuracy with macro-averaged precision, recall, and F1-score of 0.96 on a held-out test set, indicating balanced performance across classes. Heatmap visualisations consistently localise cracks and misalignments, supporting operator trust and triage. By combining a strong backbone with domain-specific augmentation, quantified architectural choices, and built-in interpretability, RailNet provides a reliable and efficient basis for early, automated track-fault detection, enabling more proactive maintenance scheduling and contributing to reduced accident risk.
PurposeEquity and being treated equitably are the rights of everyone. This is one thing that is not experienced by ISI globally. This research examines the challenges faced by the global intersex community, focusing on the specific context of Pakistan.Design/methodology/approachThrough seven intersex employees' interviews and using Rajby Industries as a case study, the study highlights the social exclusion of ISI and their determination to secure decent work and life opportunities.FindingsThe study reveals the crucial role of family support in enabling ISI to strive for normal lives. Organizational policies, exemplified by Rajby Industries, play a significant role, as they provide a supportive environment for ISI to showcase their capabilities. These ISI do not live for themselves only; they live and strive for others as well.Originality/valueThe study emphasizes that tapping into this hidden human capital can contribute to achieving sustainable developmental goals, particularly SDG 05 (Gender Equality), SDG 08 (Decent Work and Economic Growth), and SDG 10 (Reduced Inequalities). Overall, recognizing and addressing the unique challenges faced by ISI is crucial for fostering a more equitable and sustainable future.
In the domain of the Internet of Medical Things (IoMT), upholding the confidentiality, integrity, and availability of sensitive medical data is of utmost importance. However, the intricate network of interconnected IoMT devices presents formidable challenges in effectively identifying intrusions and categorizing attacks. This research project focuses on harnessing the capabilities of both machine learning and deep learning techniques to develop robust systems for intrusion detection and attack classification, specifically tailored to IoMT environments. Through the implementation of cutting-edge algorithms and methodologies, our objective is to strengthen the security framework of IoMT systems, thus ensuring the protection of patient data against unauthorized access, tampering, and disruptions in service. By conducting thorough experimentation and analysis, we aim to assess the performance of various models and methodologies, with the ultimate aim of achieving high levels of detection accuracy while minimizing false positives and false negatives. Ultimately, our research endeavors to drive forward progress in IoMT security, contributing to the creation of safer and more dependable healthcare delivery systems.
